Basic idea
This walkthrough demonstrates how to set up and run an agentic retrieval-augmented generation (RAG)
project using NVIDIA AI Workbench. You'll use AI Workbench to clone and run a pre-built agentic RAG
application that intelligently routes queries, evaluates responses for relevancy and hallucination, and
iterates through evaluation and generation cycles. The project uses a Gradio web interface and can work
with both NVIDIA-hosted API endpoints or self-hosted models.
What you'll accomplish
You'll have a fully functional agentic RAG application running in NVIDIA AI Workbench with a web
interface where you can submit queries and receive intelligent responses. The system will demonstrate
advanced RAG capabilities including query routing, response evaluation, and iterative refinement,
giving you hands-on experience with both AI Workbench's development environment and sophisticated RAG
architectures.
What to know before starting
- Basic familiarity with retrieval-augmented generation (RAG) concepts
- Understanding of API keys and how to generate them
- Comfort working with web applications and browser interfaces
- Basic understanding of containerized development environments
Prerequisites
- DGX Spark system with NVIDIA AI Workbench installed or ready to install
- Free NVIDIA API key: Generate at NGC API Keys
- Free Tavily API key: Generate at Tavily
- Internet connection for cloning repositories and accessing APIs
- Web browser for accessing the Gradio interface
Verification commands
- Verify the NVIDIA AI Workbench application exists on your DGX Spark system
- Verify your API keys are valid and up-to-date
Time & risk
- Estimated time: 30-45 minutes (including AI Workbench installation if needed)
- Risk level: Low - Uses pre-built containers and established APIs
- Rollback: Simply delete the cloned project from AI Workbench to remove all components. No system changes are made outside the AI Workbench environment.
